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-**Basic Goals and Principles - Recommendation 0 **
-===== Base Recommendation to develop and activate a FAIR Dataspace for the Helmholtz Association =====
-==== Description ====
-[Status: Under development, Date: 2023-08-25, Version: 001]
-==== Motivation for this Recommendation: ====
-The Helmholtz Association is determined to make their data available according to the FAIR principles, thus making it findable, accessible, interoperable and reusable. In order to achieve interoperability of datasets among various data infrastructures (DIS) within the Helmholtz Association,
-  * a common and agreed procedure to refer to common information is needed.
-  * common semantic concepts need to be implemented
-  * common data exchange containers and formats need to be defined and implemented
-  * common interfaces and data exchange protocols need to be agreed upon
-These following recommendations aim to describe procedures to achieve the above-mentioned goals, and identify activities and relevant stakeholder groups to implement these.
-As these goals require certain decisions to be made, we describe the decision processes from high-level to lower levels, resulting in agreed procedures. We then describe the conduct of these procedures with respect to the involved stakeholder groups, their responsibilities and actions needed to be taken.
-==== Recommendation ====
-Implement the **FAIR building blocks**:
-  * common use of **PID metadata** to harmonize common metadata (M)
-  * common use of **standardized interfaces** in repositories and data products to harvest and exchange data (I)
-  * common use of **semantic resources** (S) e.g. vocabularies, ontologies.
-  * use of **data containers**, e.g. FDOs or data crates, to achieve machine actionability (C)
-**Other important topics** to harmonize procedures are:
-  * well defined **licenses** (L)
-  * **quality** assessment and control (Q)
-  * **provenance** tracking (P)
-  * stakeholder **roles and responsibilities** (R)
-  * **valuing RDM engagement** (V) by treating published data similar to other publications including DOIs and author references, citable, with licenses and credits to the authors. This includes accountability for publishing credit systems and others. Data citations improve the data set author’s score.